Data to the People — The Case for Distributed Business Intelligence

The fact that corporations around the world are embracing business intelligence (BI) should come as no surprise. As you’d imagine, the advanced analytics developed by world-class BI practitioners leads to deeper insight and significantly enhanced performance.

But decidedly newsworthy is the degree to which the most successful BI programs are migrating toward a self-service, distributed BI delivery model—a sort of analytical enablement.

A recent Forbes Insights report, “Analytical Enablement: How Leaders Harness Distributed Intelligence to Drive Breakthrough Results,”—sponsored by Qlik and based on a global survey of 449 IT and business professionals—reveals that the most successful BI programs are significantly more likely to feature a distributed model for analysis. The term “distributed” in this context describes a condition where data is made available to, and can be modeled and analyzed by, business units themselves.

Note that in a distributed BI environment, data governance—protecting its integrity, reliability, completeness and security—can still be maintained by a central IT or BI function. Moreover, a central BI team can still play a critical or even leading role in assisting executives throughout the firm with analysis and perhaps the development of fundamental dashboards.

But the difference is one of enablement versus control. In a distributed environment, business units themselves have a greater degree of self-service access to both data and analytical solutions. In other words, those executives closest to the day-to-day business—arguably those with the most motivation to drive business improvement and the keenest intuitive insight—have greater freedom to model, explore and examine data at their fingertips.

Benefits from distributed data environments include greater effectiveness in identifying business opportunities, pricing, gauging productivity, optimizing sales and marketing spend—to name only a few.

Key challenges include integrating data from multiple sources, ensuring data security and working with IT to enable access to needed data.

Some examples of results achieved through distributed BI:

Delving into data points as diverse as trip utilization, on-time performance and customer service surveys, the U.K.’s international transport-focused National Express is able to rapidly assess, adjust and optimize its menu of destinations, routes, timetables and fares.

At a major insurer, after placing analysis solutions in the hands of business units, end-users overlaid dates of claims with traditional fields to identify and isolate two agents who had been falsifying claims.

Analysis of device-usage data at a major U.S. bank revealed the means to reducing printing costs by over $5 million per year.

At a leading provider of services to the asset management and banking industries, an accounting executive with no formal prior BI training was able to develop an array of dashboards providing insights so compelling that once shown to prospects, the firm was able to, nearly immediately, sign five new major clients.

Results like these are becoming the norm at companies where front-line business executives, those in the right place at the right time to translate insight into action, are given the keys to drive today’s most powerful analytics and decision-making platforms.

This article was written by Hugo Moreno from Forbes and was legally licensed through the NewsCred publisher network.